This week I worked more on the YOLOv11 model as a possible model for license plate detection. I set up a workflow to make training these models very easy on the ECE machines so that we do not waste our budget. After doing this, I trained the YOLOv11n model for both 100 and 400 epochs to get a good baseline model for testing on our raspberry pi and getting to MVP as quickly as possible. The models can be found here. The precision of the 400 epoch model is 0.984 and the recall is 0.964 on the validation dataset. After training, I researched the best model file types to which I should export my trained model, and I discovered that TensorFlow Lite is a good option, especially for edge devices. After some technical problems, I was able to export the Pytorch-trained model to ONNX and then to TFLite. I tried to export with INT8 quantization to further improve performance on an edge device, however, I faced many difficulties and was not able to do it.
My progress is on schedule. Next week I hope to load the model onto the Raspberry Pi and test its performance metrics on the device, specifically on how long inference takes, so that I can figure out if I want to use a different size model or if I need to get quantization working.